Type
ThesisAuthors
Alahmadi, Hamzah
Advisors
Salama, Khaled N.
Committee members
He, Jr-Hau
Gao, Xin

Program
Electrical EngineeringDate
2017-10Permanent link to this record
http://hdl.handle.net/10754/626192
Metadata
Show full item recordAbstract
In the era of Internet of Things and Big Data, unconventional techniques are rising to accommodate the large size of data and the resource constraints. New computing structures are advancing based on non-volatile memory technologies and different processing paradigms. Additionally, the intrinsic resiliency of current applications leads to the development of creative techniques in computations. In those applications, approximate computing provides a perfect fit to optimize the energy efficiency while compromising on the accuracy. In this work, we build probabilistic adders based on stochastic memristor. Probabilistic adders are analyzed with respect of the stochastic behavior of the underlying memristors. Multiple adder implementations are investigated and compared. The memristive probabilistic adder provides a different approach from the typical approximate CMOS adders. Furthermore, it allows for a high area saving and design exibility between the performance and power saving. To reach a similar performance level as approximate CMOS adders, the memristive adder achieves 60% of power saving. An image-compression application is investigated using the memristive probabilistic adders with the performance and the energy trade-off.ae974a485f413a2113503eed53cd6c53
10.25781/KAUST-M506A